High Dimensional Time Series Analysis Tools
Identifying cointegration rank of given time series
Estimation of matrix CP-factor model
Data generate process of matrix CP-factor model
Factor modeling: Inference for the number of factors
High dimensional stochastic regression with latent factors
Testing for martingale difference hypothesis in high dimension
Principal component analysis for time serise
Statistical inference for high-dimensional spectral density matrix
Statistical inference for high-dimensional spectral density matrix
Testing for unit roots based on sample autocovariances
Testing for white noise hypothesis in high dimension
Procedures for high-dimensional time series analysis including factor analysis proposed by Lam and Yao (2012) <doi:10.1214/12-AOS970> and Chang, Guo and Yao (2015) <doi:10.1016/j.jeconom.2015.03.024>,martingale difference test proposed by Chang, Jiang and Shao (2022) <doi:10.1016/j.jeconom.2022.09.001> in press,principal component analysis proposed by Chang, Guo and Yao (2018) <doi:10.1214/17-AOS1613>, identifying cointegration proposed by Zhang, Robinson and Yao (2019) <doi:10.1080/01621459.2018.1458620>, unit root test proposed by Chang, Cheng and Yao (2021) <doi:10.1093/biomet/asab034>, white noise test proposed by Chang, Yao and Zhou (2017) <doi:10.1093/biomet/asw066>, CP-decomposition for high-dimensional matrix time series proposed by Chang, He, Yang and Yao(2023) <doi:10.1093/jrsssb/qkac011> and Chang, Du, Huang and Yao (2024+), and Statistical inference for high-dimensional spectral density matrix porposed by Chang, Jiang, McElroy and Shao (2023) <doi:10.48550/arXiv.2212.13686>.